(Ebook) Handbook of Monte Carlo Methods by Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev ISBN 9780470177938, 0470177934, B00D9NT5B0

(Ebook) Handbook of Monte Carlo Methods by Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev ISBN 9780470177938, 0470177934, B00D9NT5B0

A comprehensive overview of Monte Carlo (MC) simulation that explores the latest topics, techniques, and real-world applications

More and more of today’s numerical problems found in engineering and finance are solved through MC methods. The heightened popularity of these methods makes it important for researchers to have a comprehensive understanding of the MC approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.

The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Chapters discuss key MC topics and methods, including:

Random variable and stochastic process generation

Markov chain MC, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run

Discrete-event simulation

Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation

Variance reduction, including importance sampling, latin hypercube sampling, and conditional MC

Estimation of derivatives and sensitivity analysis

Advanced topics including cross-entropy, rare events, kernel density estimation, quasi MC, particle systems, and randomized optimization

Concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing hands-on work with the material and also features the author's own lecture notes.

Appendices provide background material on probability theory, stochastic processes, and mathematical statistics, and the key optimization concepts and techniques.

Excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use MC in their research. Also a suitable supplement for courses on MC methods and computational statistics at the upper-undergraduate and graduate levels.